改进的遗传算法求解桁架的拓扑优化

提出了一种改进的遗传算法,用于优化具有离散尺寸、连续形状和0-1拓扑变量的桁架问题。考虑到离散和连续变量的本质,文中提出了混合编码方法,其中包括二进制和实数编码,整数和实数编码。本文采用了凝聚选择法-基于约束和适应度值双重标准,完全适应约束问题的本质。在优化过程中,初始种群和算子具有不确定性,因此有必要检验结构拓扑的合理性。为了增强算法的可靠性,采用了改进的重新开始算子,引入新基因并且探索新空间。求解了典型的算例,证明改进的遗传算法是可行且有效的。

Improved genetic algorithm for topology optimization of truss structures

This paper presents an improved genetic algorithm(GA) to minimize weight of truss with discrete sizing,continuous shape and 0-1 topology variables. Because of the nature of discrete and continuous variables,mixed coding schemes are proposed,including binary and float coding,integer and float coding.Surrogate reproduction is developed to select good individuals to mating pool on the basis of constraint and fitness values,which completely considers the character of constrained optimization.Because the initial population is created randomly and three operators of GA are also indeterminable,it is necessary to check whether the structural topology is desirable.An improved restart operator is proposed to introduce new gene and explore new space,so that the reliability of GA is enhanced.Standard examples are solved,numerical solutions are better than those in the literature.It is demonstrated that the improved GA scheme is feasible and effective.